753 research outputs found
Three-dimensional in situ observations of compressive damage mechanisms in syntactic foam using X-ray microcomputed tomography
Royal Society Grant number RG140680 Lloyd's Register Foundation (GB) Oil and Gas Academy of Scotland Open access via Springer Compact AgreementPeer reviewedPublisher PD
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
In this paper we explore how actor-critic methods in deep reinforcement
learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be
extended with agent modeling. Inspired by recent works on representation
learning and multiagent deep reinforcement learning, we propose two
architectures to perform agent modeling: the first one based on parameter
sharing, and the second one based on agent policy features. Both architectures
aim to learn other agents' policies as auxiliary tasks, besides the standard
actor (policy) and critic (values). We performed experiments in both
cooperative and competitive domains. The former is a problem of coordinated
multiagent object transportation and the latter is a two-player mini version of
the Pommerman game. Our results show that the proposed architectures stabilize
learning and outperform the standard A3C architecture when learning a best
response in terms of expected rewards.Comment: AAAI Conference on Artificial Intelligence and Interactive Digital
Entertainment (AIIDE'19
The influence of pulsed laser powder bed fusion process parameters on Inconel 718 material properties
Funding This publication was made possible by the sponsorship and support of Lloyd's Register Foundation, United Kingdom. The work was enabled through, and undertaken at, the National Structural Integrity Research Centre (NSIRC) United Kingdom, a postgraduate engineering facility for industry-led research into structural integrity established and managed by TWI through a network of both national and international Universities. Lloyd’s Register Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Data availability The raw data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study. The processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.Peer reviewedPostprin
Action Guidance with MCTS for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years,
however, one main challenge is the sample inefficiency. In this paper, we focus
on how to use action guidance by means of a non-expert demonstrator to improve
sample efficiency in a domain with sparse, delayed, and possibly deceptive
rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a
new framework where even a non-expert simulated demonstrator, e.g., planning
algorithms such as Monte Carlo tree search with a small number rollouts, can be
integrated within asynchronous distributed deep reinforcement learning methods.
Compared to a vanilla deep RL algorithm, our proposed methods both learn faster
and converge to better policies on a two-player mini version of the Pommerman
game.Comment: AAAI Conference on Artificial Intelligence and Interactive Digital
Entertainment (AIIDE'19). arXiv admin note: substantial text overlap with
arXiv:1904.05759, arXiv:1812.0004
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years, but
there are still open challenges, such as convergence to locally optimal
policies and sample inefficiency. In this paper, we contribute a novel
self-supervised auxiliary task, i.e., Terminal Prediction (TP), estimating
temporal closeness to terminal states for episodic tasks. The intuition is to
help representation learning by letting the agent predict how close it is to a
terminal state, while learning its control policy. Although TP could be
integrated with multiple algorithms, this paper focuses on Asynchronous
Advantage Actor-Critic (A3C) and demonstrating the advantages of A3C-TP. Our
extensive evaluation includes: a set of Atari games, the BipedalWalker domain,
and a mini version of the recently proposed multi-agent Pommerman game. Our
results on Atari games and the BipedalWalker domain suggest that A3C-TP
outperforms standard A3C in most of the tested domains and in others it has
similar performance. In Pommerman, our proposed method provides significant
improvement both in learning efficiency and converging to better policies
against different opponents.Comment: AAAI Conference on Artificial Intelligence and Interactive Digital
Entertainment (AIIDE'19). arXiv admin note: text overlap with
arXiv:1812.0004
Representative volume element (RVE) based crystal plasticity study of void growth on phase boundary in titanium alloys
Author is thankful to University of Aberdeen for the award of Elphinstone Scholarship which covers the tuition fee of PhD study of author.Peer reviewedPostprin
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